Empennage control method and device, controller, vehicle and storage medium

Abstract

The embodiment of the invention discloses an empennage control method and device, a controller, a vehicle and a storage medium. The method comprises the steps of predicting an empennage opening angle corresponding to a target vehicle based on vehicle driving information of the target vehicle; and according to the empennage opening angle, controlling an empennage configured on the target vehicle to perform angle adjustment. Therefore, according to the vehicle driving information of the target vehicle, the empennage opening angle conforming to the actual driving state of the vehicle can be generated, the generated empennage opening angle can be any angle, accurate control over the electric empennage of the vehicle is achieved, and the empennage control efficiency is improved.

Description

Technical Field

[0001] The present application relates to the field of vehicle technology, and in particular to a tail wing control method, device, controller, vehicle and storage medium. Background Art

[0002] An electric rear wing is an aerodynamic device mounted on the rear of a vehicle. Adjusting the wing's opening angle changes airflow around the vehicle, thereby improving vehicle stability. Existing rear wing control methods often determine the wing's opening angle based on vehicle speed and a preset speed-dependent angle, thereby controlling the wing's angle.

[0003] However, during the research and practice of the existing technology, it was found that the existing tail wing control method cannot accurately control the electric tail wing of the vehicle, resulting in poor tail wing control efficiency. Summary of the Invention

[0004] The embodiments of the present application provide a tail wing control method, device, controller, vehicle and storage medium, which can generate a tail wing opening angle that conforms to the actual driving state of the vehicle, and the generated tail wing opening angle can be any angle, thereby achieving accurate control of the vehicle's electric tail wing and improving the efficiency of tail wing control.

[0005] In order to achieve the above-mentioned object, according to a first aspect of the present application, a tail control method is provided, the method comprising:

[0006] Predicting a tail wing opening angle corresponding to the target vehicle based on vehicle driving information of the target vehicle;

[0007] According to the tail wing opening angle, the tail wing configured on the target vehicle is controlled to adjust the angle.

[0008] According to a second aspect of the present application, a tail control device is provided, the device comprising:

[0009] A prediction module, configured to predict a tail wing opening angle corresponding to the target vehicle based on vehicle driving information of the target vehicle;

[0010] The control module is used to control the tail wing configured on the target vehicle to adjust the angle according to the opening angle of the tail wing.

[0011] According to a third aspect of the present application, a controller is provided, comprising a processor and a memory, wherein the memory stores an application program, and the processor is configured to execute the application program in the memory to implement the tail control method provided in an embodiment of the present application.

[0012] According to a fourth aspect of the present application, a vehicle is provided, comprising the controller provided in the third aspect of the present application.

[0013] According to a fifth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and the computer program is suitable for being loaded by a processor to execute the steps of any tail control method provided in the embodiments of the present application.

[0014] According to a sixth aspect of the present application, a computer program product is provided, comprising a computer program stored in a computer-readable storage medium; when a processor of a controller reads the computer program from the computer-readable storage medium, the processor executes the computer program, causing the controller to perform the steps in the tail control method provided in an embodiment of the present application.

[0015] In the tail wing control method, device, controller, vehicle, and storage medium of the embodiments of the present application, the tail wing opening angle corresponding to the target vehicle is predicted based on the target vehicle's driving information; and the tail wing configured on the target vehicle is controlled to adjust its angle based on the tail wing opening angle. In this way, based on the target vehicle's driving information, a tail wing opening angle that matches the vehicle's actual driving state can be generated, and the generated tail wing opening angle can be any angle, achieving accurate control of the vehicle's electric tail wing and improving tail wing control efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 This is a schematic diagram of an implementation scenario of a tail wing control method provided in an embodiment of the present application;

[0018] Figure 2 1 is a flow chart of a tail wing control method provided in an embodiment of the present application;

[0019] Figure 3 Schematic diagram of a model training architecture of a tail wing control method provided in an embodiment of the present application;

[0020] Figure 4 This is a schematic diagram of the overall architecture of a tail wing control method provided in an embodiment of the present application;

[0021] Figure 5Schematic diagram of the structure of the tail control device provided in an embodiment of the present application;

[0022] Figure 6 It is a schematic diagram of the structure of the controller provided in an embodiment of the present application. DETAILED DESCRIPTION

[0023] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the embodiments described are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0024] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more features. In the description of this application, "plurality" means two or more, unless otherwise specifically defined.

[0025] The present invention provides a tail control method, device, controller, vehicle, and storage medium. The tail control device can be integrated into a controller, which can be an electronic device, such as a server or a terminal.

[0026] Among them, the server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, network acceleration services (Content Delivery Network, CDN), as well as basic cloud computing services such as big data and artificial intelligence platforms. Terminals may include but are not limited to mobile phones, computers, intelligent voice interaction devices, smart home appliances, vehicle-mounted terminals, aircraft, etc. Terminals and servers can be directly or indirectly connected through wired or wireless communication, and this application does not impose any restrictions on this.

[0027] The controller may be integrated into a vehicle, which may be a fuel vehicle, a plug-in hybrid vehicle, a new energy vehicle, etc. This application does not impose any specific limitation on this.

[0028] See also Figure 1 , taking the tail wing control device integrated into the controller as an example, Figure 1Schematic diagram of the implementation scenario of the tail wing control method provided in an embodiment of the present application, wherein the controller can be integrated into the vehicle or communicated with the vehicle, and can predict the tail wing opening angle corresponding to the target vehicle based on the vehicle driving information of the target vehicle; according to the tail wing opening angle, the tail wing configured on the target vehicle is controlled to adjust the angle.

[0029] It should be noted that Figure 1 The schematic diagram of the implementation environment scenario of the tail control method shown is merely an example. The implementation environment scenario of the tail control method described in the embodiment of this application is intended to more clearly illustrate the technical solution of the embodiment of this application and does not constitute a limitation of the technical solution provided by the embodiment of this application. Those skilled in the art will appreciate that with the evolution of tail control and the emergence of new business scenarios, the technical solution provided in this application is equally applicable to similar technical problems.

[0030] The solutions provided in the embodiments of the present application are specifically described by the following embodiments. It should be noted that the description order of the following embodiments does not limit the preferred order of the embodiments.

[0031] This embodiment will be described from the perspective of a tail wing control device, which may be integrated into a controller.

[0032] See also Figure 2 , Figure 2 FIG. 1 is a flow chart of a tail wing control method provided in an embodiment of the present application. The tail wing control method includes:

[0033] Step S101 : predicting the tail wing opening angle corresponding to the target vehicle based on the vehicle driving information of the target vehicle.

[0034] The target vehicle may be a vehicle currently undergoing tail wing control. The vehicle driving information may be information collected during the driving of the target vehicle. The tail wing opening angle may be the opening angle of a tail wing configured on the target vehicle, and may be used to adjust the opening angle of the tail wing configured on the target vehicle. The tail wing may be an electric tail wing.

[0035] The electric rear wing is an aerodynamic device mounted on the rear of the vehicle. It actively adjusts its opening angle based on the vehicle's driving conditions, thereby altering airflow over the vehicle. This disruptive flow improves vehicle stability, increases grip, reduces drag, and enhances safety and fuel efficiency. The main function of the rear wing is to provide downward pressure during driving, improving the vehicle's grip and stability.

[0036] Optionally, the vehicle driving information may include at least two of vehicle operation information, vehicle environment information and user instruction information.

[0037] Among them, the vehicle operation information can be information indicating the operating status of the target vehicle, the vehicle environment information can be information describing the driving environment of the target vehicle, and the user command information can be information indicating the command input by the user. The command input by the user may include the command triggered by the user to control the opening angle of the tail wing. The command can be obtained by collecting the sound emitted by the user, or can be obtained through buttons and applications (APPs).

[0038] In one embodiment, the vehicle operation information may include at least one of vehicle speed and acceleration, and the vehicle environment information may include at least one of wind speed, wind direction, air humidity, ground humidity, rainfall, ground water storage, downforce, grip and driving slope collected during the driving process of the target vehicle.

[0039] The driving slope may be a slope when the target vehicle is driving uphill.

[0040] Optionally, the downforce generated by the rear wing can be calculated using the following formula:

[0041] F=kρv 2 S

[0042] Here, F represents the downforce generated by the rear wing. The calculation formula for rear wing downforce can be derived based on the formula for calculating wing lift. k represents the downforce coefficient provided by the rear wing, ρ represents the air density, v represents the target vehicle's speed, and S represents the reference area of the rear wing, which can be referred to as the actual effective surface area of the rear wing. The downforce provided by the rear wing varies with the rear wing's opening angle. The pressure generated by the rear wing increases approximately proportionally within a certain range of rear wing opening angles. Beyond the upper limit, the pressure first decreases slowly and then sharply. Therefore, the rear wing primarily affects the downforce and grip at the rear of the vehicle, thus affecting vehicle handling. Factors influencing the rear wing include vehicle speed, air density, and other information. Therefore, rear wing adjustment requires comprehensive consideration of factors that affect the rear wing during vehicle operation, as well as factors that affect changes in the rear wing angle, to achieve accurate rear wing control.

[0043] Among them, based on the vehicle driving information of the target vehicle, there are many ways to predict the tail wing opening angle corresponding to the target vehicle. For example, the vehicle driving information of the target vehicle can be feature extracted to obtain vehicle feature information; through the tail wing angle prediction model, the tail wing opening angle corresponding to the target vehicle is generated based on the vehicle feature information.

[0044] The vehicle characteristic information may be characteristic information extracted from vehicle driving information. The tail wing angle prediction model may be a model for predicting the opening angle of the tail wing based on the vehicle driving information.

[0045] Optionally, the tail angle prediction model may be a neural network model, for example, a neural network model based on a multilayer perceptron (MLP). The tail angle prediction model may be referred to as a neural network controller.

[0046] Optionally, the tail angle prediction model may also be a fuzzy controller (FC) or other multi-sensor fusion controller.

[0047] Among them, there are many ways to extract features from the vehicle driving information of the target vehicle and obtain vehicle feature information. For example, the vehicle driving information of the target vehicle can be collected by at least one collection device; sub-feature information of at least one sub-information in the collected vehicle driving information can be extracted; and the sub-feature information can be fused to obtain vehicle feature information corresponding to the target vehicle.

[0048] The collection device can be a device configured for the target vehicle to collect vehicle driving information, such as various sensor devices. For example, it can include an airspeed sensor, a downforce sensor, a grip sensor, a vehicle speed sensor, an acceleration sensor, a slope sensor, a humidity sensor, a rain sensor, a sound sensor, and other sensor devices. The sub-information can be information within the vehicle driving information and can be derived from vehicle operation information, vehicle environment information, and user command information. Specifically, it can include sub-information such as the target vehicle's speed, acceleration, wind speed, wind direction, air humidity, ground humidity, rainfall, ground water content, downforce, grip, and driving slope during driving. The sub-feature information can be feature information extracted from the sub-information.

[0049] Optionally, the sub-feature information may be represented by a vector. Correspondingly, the step of fusing the sub-feature information to obtain vehicle feature information corresponding to the target vehicle may include: concatenating the vectors corresponding to each sub-feature information to obtain vehicle feature information.

[0050] In one embodiment, feature extraction processing of vehicle driving information may refer to formally organizing the sub-information in the vehicle driving information so that it can be put into a neural network or other type of model for prediction and recognition after basic processing (such as filtering and noise reduction). For example, taking the tail angle prediction model as a neural network model, the N sub-information collected by N sensors can be simply processed into N×1 vectors, and then all the vectors are spliced, and the spliced feature matrix is used as vehicle feature information, where each row in the feature matrix represents a sub-information collected by a sensor and is input into the neural network model. Optionally, if a more complex neural network model is required, or a more complex prediction mechanism is required, the above-mentioned N*1 matrix can be further processed. For example, assuming that the N*1 matrix is A, A can be calculated. T A, to further generate N*N feature matrix, etc.

[0051] Optionally, feature extraction of the collected vehicle driving information for tail wing angle prediction may be performed only when the electric tail wing configured on the target vehicle is activated. Specifically, before extracting sub-feature information of at least one sub-information in the collected vehicle driving information, a step of detecting whether the tail wing configured on the target vehicle is activated may be performed; if the tail wing is activated, the step of extracting sub-feature information of at least one sub-information in the collected vehicle driving information is performed.

[0052] After extracting features from the target vehicle's driving information and obtaining vehicle characteristic information, the tail wing opening angle corresponding to the target vehicle can be generated based on the vehicle characteristic information using a tail wing angle prediction model. There are various ways to generate the tail wing opening angle corresponding to the target vehicle based on the vehicle characteristic information using the tail wing angle prediction model. For example, based on user usage, the weight of user command information in the neural network can be adjusted compared to other vehicle driving information. Specifically, weight information set for the user command information can be determined; and the tail wing opening angle corresponding to the target vehicle can be generated based on the weight information and the vehicle characteristic information using the tail wing angle prediction model.

[0053] The weight information may be information indicating a weight set for the user instruction information.

[0054] There are many ways to determine the weight information set for the user instruction information. For example, the user can set the weight through an application or a button provided on the target vehicle, so as to obtain the weight information set for the user instruction information.

[0055] In one embodiment, there may be multiple ways to determine the weight information set for the user command information. For example, the target vehicle may be configured with multiple tail control gears, and the weights set for the user command information under different tail control gears are different. In this way, in response to the selection operation of the tail control gear, the selected target tail control gear can be determined; based on the weight corresponding to the target tail control gear, the weight information set for the user command information is determined.

[0056] The tail control gear can be a gear used to set the weight of user command information. Different tail control gears have different weights set for user command information. In this way, the user can set corresponding weights for user command information by selecting different tail control gears. The target tail control gear can be the tail control gear selected by the user.

[0057] Among them, there are many ways to generate the tail wing opening angle corresponding to the target vehicle based on the weight information and vehicle characteristic information through the tail wing angle prediction model. For example, based on the weight information, the weight parameters of the tail wing angle prediction model corresponding to the target vehicle can be updated to obtain the target tail wing angle prediction model; through the target tail wing angle prediction model, the tail wing opening angle corresponding to the target vehicle is generated based on the vehicle characteristic information.

[0058] The weight parameters can be weights in the neural network of the tail angle prediction model. Neural network weights are parameters that connect individual neuron nodes within the neural network and represent the strength of the connections between them. Neural network weights can be used to perform a weighted summation of local regions of input data during convolution operations, thereby determining the importance of each input feature to the output feature. The target tail angle prediction model can be a tail angle prediction model adjusted based on the weight information.

[0059] Optionally, there are multiple ways to generate the tail wing opening angle corresponding to the target vehicle through the tail wing angle prediction model based on the weight information and vehicle characteristic information. For example, the target vehicle can be configured with multiple tail wing angle prediction models, and each tail wing angle prediction model can correspond to a different weight. In this way, the target tail wing angle prediction model can be determined from the multiple tail wing angle prediction models based on the weight information; and the tail wing opening angle corresponding to the target vehicle can be generated based on the vehicle characteristic information through the target tail wing angle prediction model.

[0060] The target tail angle prediction model may be a tail angle prediction model corresponding to the weight information. Based on the weight information, the weight set by the user for the user instruction information can be determined, thereby selecting a tail angle prediction model with the same weight as the weight set for the user instruction information from the tail angle prediction models and selecting it as the target tail angle prediction model.

[0061] Optionally, to ensure driving safety, when the vehicle is currently in a dangerous or high-risk driving situation or environment, the weight set for the user command information may not be used to control the rear wing angle, so as to prevent the user command from being contrary to the rear wing angle adjustment trend used to increase safety in the current high-risk environment or driving situation, thereby producing worse results. For example, when the vehicle seriously lacks rear downforce, if the rear wing needs to be closed based on the user command information, this will cause the vehicle to slip or lose control due to lack of downforce.

[0062] Among them, there are many ways to generate the tail wing opening angle corresponding to the target vehicle based on the weight information and vehicle characteristic information through the tail wing angle prediction model. For example, the driving state of the target vehicle can be determined based on the vehicle driving information; if the driving state is a high-risk driving state, the tail wing opening angle corresponding to the target vehicle is generated based on the vehicle characteristic information through the tail wing angle prediction model; if the driving state is a low-risk driving state, the tail wing opening angle corresponding to the target vehicle is generated based on the weight information and vehicle characteristic information through the tail wing angle prediction model.

[0063] Among them, the driving status can indicate the status of the target vehicle during driving, and the driving status can include a high-risk driving status and a low-risk driving status. The high-risk driving status can refer to a driving status with a higher safety risk, for example, the vehicle is currently in a dangerous driving situation or a high-risk environment, and the low-risk driving status can refer to a safer driving state.

[0064] In one embodiment, to ensure driving safety, the weight assigned to user commands can be reduced when the vehicle is currently in a dangerous or high-risk driving situation or environment. This prevents the user's command from conflicting with the tail wing angle adjustment intended to increase safety in the current high-risk environment or driving situation, thereby preventing a worsening outcome. This allows the user to set the weight of user commands to the highest possible value without compromising driving safety.

[0065] Specifically, without compromising driving safety, the tail wing angle prediction model can be configured to prioritize user commands when processing input vehicle characteristic information. This means that the resulting tail wing angle prediction is more dependent on user commands, or even generated entirely based on them, thereby controlling the tail wing angle based on the user commands. In this case, vehicle driving information collected by other sensors is not considered. However, if data collected by other sensors indicates that the target vehicle is currently in a high-risk driving state, the weight assigned to the user commands will be lowered to prevent the user commands from conflicting with the tail wing angle adjustment intended to enhance safety in these high-risk driving states, resulting in a worse control outcome.

[0066] Optionally, the weight corresponding to the user's command information and the risk of the surrounding driving environment can be fed back to the user in real time, so that the user can understand the driving status of the target vehicle and the current tail wing control situation, avoiding the user's feeling of out-of-control operation, thereby improving the user experience.

[0067] Optionally, a switch configured on the target vehicle for the user to manually operate to control the opening and closing of the electric tail wing can have higher control authority than the controller corresponding to the tail wing angle prediction model, so that the user can manually control the opening of the tail wing in special driving conditions, thereby improving the efficiency of tail wing control.

[0068] In one embodiment, some mode gears can be configured for the target vehicle. Each mode gear does not accept the user's weight setting, and the opening and closing angle of the tail wing is adjusted automatically. For example, the mode gear may include aggressive driving mode, mild driving mode, track driving mode and other mode gears. At the same time, the weight of the aggressive driving mode, the weight of the mild driving mode and the weight of the track driving mode can be provided, so that the tail wing angle prediction model can more accurately predict the tail wing opening angle that is more in line with the current driving situation based on the selected mode gear and the corresponding weight, thereby more accurately controlling the opening angle of the electric tail wing.

[0069] In one embodiment, please refer to Figure 3 , Figure 3 This is a schematic diagram of the model training architecture of a tail wing control method provided in an embodiment of the present application, which can be used to describe the process of training a tail wing angle prediction model based on vehicle feature information established according to sensor data and corresponding tail wing opening angle labels. First, various sensors and uploaded data from buttons or applications on the vehicle can be collected, including at least: wind speed, wind direction, air and ground humidity, rainfall and ground water storage, downforce, grip, vehicle speed, acceleration, slope and user command information, and these data can be integrated into a feature matrix that can comprehensively reflect the characteristics of the vehicle and the characteristics of the environment in which it is located. At the same time, the appropriate electric tail wing opening and closing angles corresponding to different feature matrices can be calibrated, wherein the minimum opening angle of 0° can be used to represent the closed state. In this way, based on a large amount of road test data, simulation analysis or industry experience, a "feature-angle" data set containing a large number of feature matrices and corresponding tail wing angle labels can be created, and a suitable neural network model can be created as a tail wing angle prediction model according to task requirements, and training can be performed on the above data set. The weights of the tail wing angle prediction model are adjusted by the optimizer until a learned tail wing angle prediction model is obtained. Considering that there are many input features, a deep network model can be used to more accurately extract the relationship between features and angles.

[0070] Step S102: controlling the tail wing configured on the target vehicle to adjust the angle according to the tail wing opening angle.

[0071] Among them, there are many ways to control the angle of the tail wing configured on the target vehicle according to the tail wing opening angle. For example, the tail wing opening angle can be output to the electric tail wing drive module, and the electric tail wing drive module can be used to control the opening and closing of the tail wing to reach the angle corresponding to the tail wing opening angle.

[0072] Optionally, after controlling the angle of the tail wing configured on the target vehicle according to the tail wing opening angle, it is also possible to detect whether the weight information set for the user command information has changed; if it is detected that the weight information has changed, the tail wing angle prediction model is used to generate the target tail wing opening angle corresponding to the target vehicle based on the changed weight information and vehicle characteristic information; based on the target tail wing opening angle, the tail wing is controlled to adjust its angle.

[0073] The target tail wing opening angle may be a tail wing opening angle predicted based on the changed weight information.

[0074] There are multiple ways to control the tail wing to adjust its angle based on the target tail wing opening angle. For example, the tail wing opening angle can be controlled to gradually adjust from the tail wing opening angle to the target tail wing opening angle based on the target tail wing opening angle.

[0075] Wherein, controlling the tail wing opening angle to be gradually adjusted from the tail wing opening angle to the target tail wing opening angle may refer to that when adjusting the tail wing angle based on the target tail wing opening angle, the tail wing opening angle is not directly switched from the tail wing opening angle to the target tail wing opening angle, but rather the tail wing opening angle is gradually adjusted from the tail wing opening angle until the current opening angle is adjusted to the target tail wing opening angle. For example, assuming the target tail wing opening angle is 20° and the tail wing opening angle is 10°, when adjusting the tail wing angle based on the target tail wing opening angle, the tail wing opening angle may be controlled to be gradually increased by 1° from 10° until the tail wing opening angle is adjusted to 20°, i.e., the target tail wing opening angle.

[0076] In one embodiment, the vehicle can monitor in real time whether the weights set for user command information have changed. Once a change occurs, the weight parameters of the rear wing angle prediction model can be immediately updated, but the opening and closing angles of the rear wing are not adjusted immediately. Instead, the rear wing opening angle is slowly transitioned from the initial rear wing opening angle to the target rear wing opening angle to prevent sudden changes in downforce that may cause danger to vehicle driving, thereby effectively improving the efficiency of rear wing control.

[0077] In one embodiment, please refer to Figure 4 , Figure 4This is a schematic diagram of the overall architecture of a rear wing control method provided in an embodiment of the present application. It describes the process by which a neural network controller corresponding to a rear wing angle prediction model outputs a rear wing opening angle based on an input feature matrix. The method can be divided into two modules: a feature matrix acquisition module and a neural network controller module. The feature matrix acquisition module can be used to collect vehicle driving information in real time when the electric rear wing is activated. The neural network controller module can be used to generate the rear wing opening angle of the electric rear wing based on the feature matrix corresponding to the input vehicle feature information. The feature matrix acquisition module can also be divided into two parts: a vehicle and environmental feature acquisition module and a user command input module. The vehicle and environmental feature acquisition module is primarily used to collect non-user information, which can include vehicle operating information and information about the vehicle's environment. The body control module (BCM) acquires data collected by various sensors and processes the data to obtain various vehicle driving information. The user command input module is primarily used to collect and identify user command information. For example, it can obtain user command information triggered by keystrokes or application commands. Simultaneously, a semantic recognition module can be used to perform semantic recognition on data collected by the sound sensor to obtain user command information.

[0078] When the target vehicle starts, the tail control program monitors in real time whether the electric tail activation button is triggered, whether the activation control in the application is triggered, and whether the user voice requests the electric tail activation. When the electric tail receives the activation signal, the feature matrix acquisition module begins to operate. Based on the collected sensor data and user command information, it combines and generates a feature matrix corresponding to the vehicle's characteristics and outputs it to the neural network controller module. After receiving the feature matrix, the neural network controller module inputs it into a trained tail angle prediction model. Through its input layer, hidden layer, and output layer, it generates a corresponding tail angle prediction result, namely the tail opening angle. The tail opening angle is output to the electric tail drive module, which controls the tail opening and closing to the corresponding angle. After the tail adjustment is completed and a prediction cycle has passed, if the tail is still in the activated state, the feature matrix acquisition module continues to collect data to generate a feature matrix and transmit it to the neural network controller module. The neural network controller then continues to predict and generate a new tail opening angle and transmits it to the electric tail drive module. This cycle continues until the electric tail is no longer activated. When it is no longer activated, the electric tail is quickly closed.

[0079] The existing control methods for electric rear wings are mainly divided into manual control and automatic control. The manual control method of the electric rear wing is mainly based on the instructions of the user (driver), including button instructions, voice instructions, gesture instructions, etc. After the driver comprehensively judges the road conditions, vehicle speed, slope, grip, downforce and the user's subjective wishes, the driver outputs instructions to the electric rear wing to adjust the angle. The angle of the electric rear wing can only be a preset value. The automatic control method of the electric rear wing mainly determines whether the electric rear wing is open or closed and the opening angle based on the vehicle speed and load data uploaded by the sensor. By analyzing part or all of the above information, the electric rear wing opening and closing angle instructions are generated once or successively. At the same time, there are only a few preset opening and closing angles, and it is impossible to adjust them steplessly according to the actual situation. It can be seen that the existing rear wing control method cannot accurately control the vehicle's electric rear wing, resulting in poor rear wing control efficiency.

[0080] To this end, an embodiment of the present application provides a tail wing control method, which uses a neural network model to collect and comprehensively analyze a large amount of other vehicle-mounted sensor data, including wind direction, wind speed, air and ground humidity, rainfall and ground water storage, downforce, grip, vehicle speed, acceleration, slope and manual instructions, etc., in order to restore the actual vehicle information and the vehicle's environmental information as much as possible while ensuring automatic control of the tail wing, and generate a real-time optimal electric tail wing control angle based on this, so as to achieve the adjustment of any electric tail wing angle within a reasonable range, and not limited to angle control through only a few preset opening and closing angles, without the need for additional control by the user, but also leaving a control loop for manual control, effectively improving the efficiency of tail wing control.

[0081] As can be seen from the above, the embodiments of the present application predict the tail wing opening angle corresponding to the target vehicle based on the target vehicle's driving information; and control the tail wing configured on the target vehicle to adjust the angle based on the tail wing opening angle. In this way, based on the target vehicle's driving information, a tail wing opening angle that matches the vehicle's actual driving state can be generated, and the generated tail wing opening angle can be any angle, achieving accurate control of the vehicle's electric tail wing and improving tail wing control efficiency.

[0082] To facilitate better implementation of the tail control method provided in the embodiment of the present application, the embodiment of the present application also provides a device based on the tail control method. The meanings of the terms herein are the same as those in the tail control method, and the specific implementation details can be referred to the description in the method embodiment.

[0083] For example, Figure 5 FIG. 2 is a schematic diagram of the structure of a tail control device provided in an embodiment of the present application. The tail control device may include a prediction module 201 and a control module 202, as follows:

[0084] Prediction module 201, for predicting the tail wing opening angle corresponding to the target vehicle based on the vehicle driving information of the target vehicle;

[0085] The control module 202 is used to control the tail wing configured on the target vehicle to adjust the angle according to the tail wing opening angle.

[0086] In one embodiment, the vehicle driving information includes at least two of vehicle operation information, vehicle environment information, and user instruction information.

[0087] In one embodiment, the vehicle operation information includes at least one of vehicle speed and acceleration, and the vehicle environment information includes at least one of wind speed, wind direction, air humidity, ground humidity, rainfall, ground water storage, downforce, grip and driving slope collected during the driving process of the target vehicle.

[0088] In one embodiment, the prediction module 201 is configured to:

[0089] Extracting features from the target vehicle's driving information to obtain vehicle feature information;

[0090] Through the tail wing angle prediction model, the tail wing opening angle corresponding to the target vehicle is generated based on the vehicle feature information.

[0091] In one embodiment, the tail wing angle prediction model is used to generate the tail wing opening angle corresponding to the target vehicle based on the vehicle characteristic information, specifically for:

[0092] Determining weight information set for user instruction information;

[0093] Through the tail wing angle prediction model, the tail wing opening angle corresponding to the target vehicle is generated based on the weight information and vehicle feature information.

[0094] In one embodiment, the tail wing angle prediction model generates the tail wing opening angle corresponding to the target vehicle based on the weight information and vehicle characteristic information, specifically for:

[0095] Based on the weight information, the weight parameters of the tail wing angle prediction model corresponding to the target vehicle are updated to obtain the target tail wing angle prediction model;

[0096] The target tail wing angle prediction model is used to generate the tail wing opening angle corresponding to the target vehicle based on the vehicle feature information.

[0097] In one embodiment, a target vehicle is configured with multiple tail wing angle prediction models, each tail wing angle prediction model having a different weight. The tail wing angle prediction model generates the tail wing opening angle corresponding to the target vehicle based on the weight information and vehicle characteristic information, specifically for:

[0098] determining a target tail angle prediction model among multiple tail angle prediction models based on the weight information;

[0099] The target tail wing angle prediction model is used to generate the tail wing opening angle corresponding to the target vehicle based on the vehicle feature information.

[0100] In one embodiment, the tail wing angle prediction model generates the tail wing opening angle corresponding to the target vehicle based on the weight information and vehicle characteristic information, specifically for:

[0101] Determining the driving state of the target vehicle based on the vehicle driving information;

[0102] If the driving state is high-risk, the tail wing angle prediction model is used to generate the tail wing opening angle corresponding to the target vehicle based on the vehicle feature information;

[0103] If the driving state is low-risk, the tail wing angle prediction model is used to generate the tail wing opening angle corresponding to the target vehicle based on the weight information and vehicle feature information.

[0104] In one embodiment, the target vehicle is configured with multiple tail wing control gears, and different weights are set for user command information in different tail wing control gears. The weight information set for the user command information is specifically used to:

[0105] In response to a selection operation of a tail control gear, determining a selected target tail control gear;

[0106] Based on the weight corresponding to the target tail control gear, weight information set for the user instruction information is determined.

[0107] In one embodiment, the tail control device further includes a weight updating module for:

[0108] Detect whether the weight information set for the user instruction information has changed;

[0109] If a change in weight information is detected, the tail wing angle prediction model is used to generate a target tail wing opening angle corresponding to the target vehicle based on the changed weight information and vehicle feature information;

[0110] Based on the target tail opening angle, the tail is controlled to adjust the angle.

[0111] In one embodiment, the control of the tail wing to adjust the angle based on the target tail wing opening angle is specifically used to:

[0112] Based on the target tail wing opening angle, the tail wing opening angle is controlled to be gradually adjusted from the tail wing opening angle to the target tail wing opening angle.

[0113] In one embodiment, the feature extraction of the target vehicle's driving information is performed to obtain vehicle feature information, which is specifically used to:

[0114] collecting vehicle driving information of a target vehicle through at least one collecting device;

[0115] Extracting sub-feature information of at least one sub-information in the collected vehicle driving information;

[0116] The sub-feature information is fused to obtain the vehicle feature information corresponding to the target vehicle.

[0117] In one embodiment, the sub-feature information is represented by a vector; the above fusion of the sub-feature information to obtain the vehicle feature information corresponding to the target vehicle is specifically used for:

[0118] The vectors corresponding to each sub-feature information are concatenated to obtain vehicle feature information.

[0119] In one embodiment, before extracting the sub-feature information of at least one sub-information in the collected vehicle driving information, the method is further specifically configured to:

[0120] Detect whether the tail wing configured on the target vehicle is in an activated state;

[0121] If the tail wing is in an activated state, the step of extracting sub-feature information of at least one sub-information in the collected vehicle driving information is performed.

[0122] As can be seen from the above, in this embodiment of the application, prediction module 201 predicts the tail wing opening angle corresponding to the target vehicle based on the target vehicle's driving information; control module 202 controls the tail wing configured on the target vehicle to adjust the angle based on the tail wing opening angle. In this way, based on the target vehicle's driving information, a tail wing opening angle that matches the vehicle's actual driving state can be generated, and the generated tail wing opening angle can be any angle, achieving accurate control of the vehicle's electric tail wing and improving tail wing control efficiency.

[0123] Accordingly, the embodiment of the present application also provides a controller, such as Figure 6 As shown, Figure 6 Schematic diagram of the structure of the controller provided in an embodiment of the present application. The controller 300 includes a processor 301 having one or more processing cores, a memory 302 having one or more computer-readable storage media, and a computer program stored in the memory 302 and executable on the processor. The processor 301 is electrically connected to the memory 302. It will be understood by those skilled in the art that the controller structure shown in the figure does not constitute a limitation of the controller, and may include more or fewer components than shown, or combine certain components, or arrange the components differently.

[0124] The processor 301 is the control center of the controller 300. It connects the various parts of the entire controller 300 using various interfaces and lines. It executes various functions of the controller 300 and processes data by running or loading software programs and / or units stored in the memory 302 and calling data stored in the memory 302. The processor 301 can be a processor CPU, a graphics processor GPU, a network processor (NP), etc., and can implement or execute the various methods, steps, and logic blocks disclosed in the embodiments of this application.

[0125] In the embodiment of the present application, the processor 301 in the controller 300 loads instructions corresponding to one or more application processes into the memory 302 according to the following steps, and the processor 301 runs the application stored in the memory 302 to implement various functions, such as:

[0126] Based on the vehicle driving information of the target vehicle, the tail wing opening angle corresponding to the target vehicle is predicted; according to the tail wing opening angle, the tail wing configured on the target vehicle is controlled to adjust the angle.

[0127] Furthermore, various functions implemented by running the application stored in the memory 302 can also be described in the aforementioned embodiments and will not be repeated here.

[0128] The specific implementation of the above operations can be found in the previous embodiments and will not be repeated here.

[0129] Optional, such as Figure 6 As shown, the controller 300 further includes: a touch screen 303, a radio frequency circuit 304, an audio circuit 305, an input unit 306, and a power supply 307. Among them, the processor 301 is electrically connected to the touch screen 303, the radio frequency circuit 304, the audio circuit 305, the input unit 306, and the power supply 307 respectively. Those skilled in the art will understand that Figure 6 The controller structure shown in the figure does not constitute a limitation to the controller, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0130] The touch display screen 303 can be used for displaying a graphical user interface and receiving the operation instructions generated by the user acting on the graphical user interface. The touch display screen 303 may include a display panel and a touch panel. Among them, the display panel can be used for displaying the information input by the user or the information provided to the user and various graphical user interfaces of the controller, and these graphical user interfaces can be composed of graphics, text, icons, videos and any combination thereof. Optionally, the display panel can be configured in the form of a liquid crystal display (LCD), an organic light emitting diode (OLED), or the like. The touch panel can be used for collecting the touch operation of the user thereon or near it (such as the user uses any suitable object or accessory such as a finger, a stylus on the touch panel or near the touch panel), and generates corresponding operation instructions, and the operation instructions execute corresponding programs. Optionally, the touch panel may include two parts: a touch detection device and a touch controller. Among them, the touch detection device detects the user's touch direction, detects the signal brought by the touch operation, and transmits the signal to the touch controller; the touch controller receives the touch information from the touch detection device, converts it into the touch point coordinates, and then sends it to the processor 301, and can receive the command sent by the processor 301 and execute it. The touch panel can cover the display panel. When the touch panel detects a touch operation on or near it, it is transmitted to the processor 301 to determine the type of touch event, and then the processor 301 provides a corresponding visual output on the display panel according to the type of touch event. In an embodiment of the present application, the touch panel and the display panel can be integrated into the touch display screen 303 to realize the input and output functions. However, in some embodiments, the touch panel and the touch panel can be used as two independent components to realize the input and output functions. That is, the touch display screen 303 can also be used as part of the input unit 306 to realize the input function.

[0131] The RF circuit 304 may be used to transmit and receive RF signals, so as to establish wireless communication with a network device or other controllers through wireless communication, and to transmit and receive signals with the network device or other controllers.

[0132] The audio circuit 305 can be used to provide an audio interface between the user and the controller via a speaker and microphone. The audio circuit 305 can convert received audio data into electrical signals and transmit them to the speaker, which then converts them into sound signals for output. The microphone, on the other hand, converts collected sound signals into electrical signals, which are then received by the audio circuit 305 and converted into audio data. The audio data is then output to the processor 301 for processing, and then sent to another controller via the RF circuit 304. Alternatively, the audio data can be output to the memory 302 for further processing. The audio circuit 305 may also include an earphone jack to allow communication between an external headset and the controller.

[0133] The input unit 306 may be configured to receive input target video and generate keyboard, mouse, joystick, optical or trackball signal input related to user settings and function control.

[0134] Power supply 307 is used to supply power to various components of controller 300. Optionally, power supply 307 can be logically connected to processor 301 via a power management system, thereby enabling the power management system to manage charging, discharging, and power consumption. Power supply 307 can also include one or more DC or AC power supplies, a recharging system, a power failure detection circuit, a power converter or inverter, a power status indicator, and other arbitrary components.

[0135] although Figure 6 Not shown in the figure, the controller 300 may also include a camera, a sensor, a wireless fidelity module, a Bluetooth module, etc., which will not be repeated here.

[0136] In the above embodiments, the descriptions of each embodiment have their own emphasis. For parts not described in detail in one embodiment, please refer to the relevant descriptions of other embodiments. It should be noted that the controller provided in the embodiments of this application and the tail control method in the above embodiments are based on the same concept. The specific implementation process is detailed in the above method embodiments and will not be repeated here.

[0137] As can be seen from the above, the controller provided in the embodiments of the present application can predict the tail wing opening angle corresponding to a target vehicle based on the target vehicle's driving information; and control the tail wing configured on the target vehicle to adjust its angle based on the tail wing opening angle. In this way, based on the target vehicle's driving information, a tail wing opening angle that matches the vehicle's actual driving state can be generated, and the generated tail wing opening angle can be any angle, achieving accurate control of the vehicle's electric tail wing and improving tail wing control efficiency.

[0138] Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be accomplished by instructions, or by controlling related hardware through instructions. The instructions may be stored in a computer-readable storage medium and loaded and executed by a processor.

[0139] To this end, an embodiment of the present application provides a computer-readable storage medium including a computer program. When the computer program is executed on a controller, the computer program is configured to cause the controller to execute any of the tail control methods provided in the embodiments of the present application. For example, the computer program may execute the following steps of the tail control method:

[0140] Based on the vehicle driving information of the target vehicle, the tail wing opening angle corresponding to the target vehicle is predicted; according to the tail wing opening angle, the tail wing configured on the target vehicle is controlled to adjust the angle.

[0141] Furthermore, for the detailed steps of the above method steps, please refer to the description in the above embodiments, which will not be repeated here.

[0142] The specific implementation of the above operations can be found in the previous embodiments and will not be repeated here.

[0143] The computer-readable storage medium may include a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0144] Since the computer program stored in the computer-readable storage medium can execute any of the tail control methods provided in the embodiments of the present application, the beneficial effects that can be achieved by any of the tail control methods provided in the embodiments of the present application can be achieved. Please refer to the previous embodiments for details and will not be repeated here.

[0145] According to one aspect of the present application, a computer program product is also provided, including a computer program, which is stored in a computer-readable storage medium; when a processor of a controller reads the computer program from the computer-readable storage medium, the processor executes the computer program, so that the controller executes the methods provided in various optional implementations of the above embodiments.

[0146] In the above-described embodiments of the tail control device, computer-readable storage medium, controller, and computer program product, the descriptions of each embodiment have different emphases. For portions not described in detail in a particular embodiment, reference can be made to the relevant descriptions of other embodiments. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes and beneficial effects of the tail control device, computer-readable storage medium, computer program product, controller, and their corresponding units described above can be referred to in the description of the tail control method in the above embodiments, and the details will not be repeated here.

[0147] The above is a detailed introduction to a tail wing control method, device, controller, vehicle, computer-readable storage medium and computer program product provided in the embodiments of the present application. Specific examples are used herein to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea; at the same time, for technical personnel in this field, based on the ideas of the present application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A tail wing control method, characterized in that: include: Predicting a tail wing opening angle corresponding to the target vehicle based on vehicle driving information of the target vehicle; According to the tail wing opening angle, the tail wing configured on the target vehicle is controlled to adjust the angle.

2. The tail wing control method according to claim 1, characterized in that: The vehicle driving information includes at least two of vehicle operation information, vehicle environment information and user instruction information.

3. The tail wing control method according to claim 2, characterized in that: The vehicle operation information includes at least one of vehicle speed and acceleration, and the vehicle environment information includes at least one of wind speed, wind direction, air humidity, ground humidity, rainfall, ground water storage, downforce, grip and driving slope collected during the driving process of the target vehicle.

4. The tail wing control method according to claim 2, characterized in that: The predicting, based on the vehicle driving information of the target vehicle, the tail wing opening angle corresponding to the target vehicle includes: Extracting features from the target vehicle's driving information to obtain vehicle feature information; The tail wing angle prediction model is used to generate the tail wing opening angle corresponding to the target vehicle based on the vehicle characteristic information.

5. The tail wing control method according to claim 4, characterized in that: The generating of the tail wing opening angle corresponding to the target vehicle based on the vehicle characteristic information by using the tail wing angle prediction model includes: Determining weight information set for the user instruction information; The tail wing angle prediction model is used to generate a tail wing opening angle corresponding to the target vehicle based on the weight information and the vehicle feature information.

6. The tail wing control method according to claim 5, characterized in that: The generating of the tail wing opening angle corresponding to the target vehicle based on the weight information and the vehicle characteristic information by using the tail wing angle prediction model includes: Based on the weight information, the weight parameters of the tail wing angle prediction model corresponding to the target vehicle are updated to obtain a target tail wing angle prediction model; The target tail wing angle prediction model is used to generate a tail wing opening angle corresponding to the target vehicle based on the vehicle characteristic information.

7. The tail wing control method according to claim 5, characterized in that: The target vehicle is configured with a plurality of tail wing angle prediction models, each tail wing angle prediction model corresponds to a different weight, and generating the tail wing opening angle corresponding to the target vehicle based on the weight information and the vehicle characteristic information through the tail wing angle prediction model includes: determining a target tail angle prediction model among the plurality of tail angle prediction models based on the weight information; The target tail wing angle prediction model is used to generate a tail wing opening angle corresponding to the target vehicle based on the vehicle characteristic information.

8. The tail wing control method according to claim 5, characterized in that: The generating of the tail wing opening angle corresponding to the target vehicle based on the weight information and the vehicle characteristic information by using the tail wing angle prediction model includes: Determining a driving state of the target vehicle based on the vehicle driving information; If the driving state is a high-risk driving state, generating a tail wing opening angle corresponding to the target vehicle based on the vehicle characteristic information through a tail wing angle prediction model; If the driving state belongs to a low-risk driving state, a tail wing opening angle corresponding to the target vehicle is generated based on the weight information and the vehicle feature information through a tail wing angle prediction model.

9. The tail wing control method according to claim 5, characterized in that: The target vehicle is configured with a plurality of tail wing control gears, and different weights are set for the user instruction information in different tail wing control gears. The weight information set for the user instruction information is determined, including: In response to the selection operation of the tail control gear, determining a selected target tail control gear; Based on the weight corresponding to the target tail control gear, weight information set for the user instruction information is determined.

10. The tail wing control method according to claim 5, characterized in that: After controlling the tail wing configured on the target vehicle to adjust the angle according to the tail wing opening angle, the method further includes: detecting whether weight information set for the user instruction information has changed; If a change in the weight information is detected, a target tail wing opening angle corresponding to the target vehicle is generated based on the changed weight information and the vehicle characteristic information through a tail wing angle prediction model; Based on the target tail opening angle, the tail is controlled to adjust its angle.

11. The tail wing control method according to claim 10, characterized in that: The controlling the tail wing to adjust the angle based on the target tail wing opening angle includes: Based on the target tail wing opening angle, the tail wing opening angle is controlled to be gradually adjusted from the tail wing opening angle to the target tail wing opening angle.

12. The tail wing control method according to claim 4, characterized in that: The feature extraction of the vehicle driving information of the target vehicle to obtain vehicle feature information includes: collecting vehicle driving information of a target vehicle through at least one collecting device; Extracting sub-feature information of at least one sub-information in the collected vehicle driving information; The sub-feature information is fused to obtain vehicle feature information corresponding to the target vehicle.

13. The tail wing control method according to claim 12, characterized in that: The sub-feature information is represented by a vector; The fusing of the sub-feature information to obtain vehicle feature information corresponding to the target vehicle includes: The vectors corresponding to each sub-feature information are concatenated to obtain vehicle feature information.

14. The tail wing control method according to claim 12, characterized in that: Before extracting sub-feature information of at least one sub-information in the collected vehicle driving information, the method further includes: Detect whether the tail wing configured on the target vehicle is in an activated state; If the tail wing is in the activated state, the step of extracting sub-feature information of at least one sub-information in the collected vehicle driving information is performed.

15. A tail wing control device, characterized in that: include: A prediction module, configured to predict a tail wing opening angle corresponding to the target vehicle based on vehicle driving information of the target vehicle; The control module is used to control the tail wing configured on the target vehicle to adjust the angle according to the opening angle of the tail wing.

16. A controller, characterized in that: The method comprises a processor and a memory, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor is enabled to perform the steps of any one of the methods of claims 1 to 14.

17. A vehicle, characterized in that: The vehicle includes the controller of claim 16.

18. A computer-readable storage medium, characterized in that The method comprises a computer program, and when the computer program is run on a controller, the computer program is used to make the controller perform the steps of the method according to any one of claims 1 to 14.

19. A computer program product, characterized in that The method comprises a computer program or instructions, which implements the steps of the method according to any one of claims 1 to 14 when executed by a processor.

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